System, method and device for automatic noninvasive screening for diabetes and pre-diabetes
AssigneeLifelens, LLC
InventorsJason Wakizaka, Wilson To, Tade Souaiaia
Life of this patent
Estimating the term…
Abstract
A system for an automatic noninvasive screening for diabetes and pre-diabetes using a device to take at least one image of a patient's eye, executing non-transitory instructions executable on a processor for analyzing the image and displaying an indication if the patient has diabetes.
Claims49
A system for an automatic noninvasive screening for diabetes and pre-diabetes using at least one image, the system comprising:
- a) an imaging device for taking at least one image of a patient's eye;
- b) a processor, operably connected to the imaging device, comprising non-transitory instructions executable on the processor for analyzing the image;
- and
- c) an indicator operably connected to the imaging device and the processor for displaying an indication if the patient has diabetes.
A device useful for a system for automatic noninvasive screening for diabetes and pre-diabetes, the device comprising:
- a) an eyepiece;
- b) a tube having a proximal and distal end and an exterior and interior portion, where the eyepiece is attached to the distal end;
- c) a light source affixed to the interior portion of the tube;
- d) a magnifying lens affixed to the interior portion near the proximal end of the tube;
- and
- e) a camera body affixed to the proximal end of the tube.
A device useful for a system for automatic noninvasive screening for diabetes and pre-diabetes, the device comprising:
- a) a handheld computer;
- b) a camera body coupled to the handheld computer;
- and
- c) a lens with a light source wrapped around the lens coupled to the camera body, where the light source can direct light to the eye in a uniform, diffuse manner, as well as minimize reflected glare.
A method for an automatic noninvasive screening for diabetes and pre-diabetes using at least one image, the method comprising the steps of:
- a) capturing a color image of a patient's eye;
- b) storing the image in a storage for processing;
- c) converting the color image to a grayscale image;
- d) applying a box blur filter to the grayscale image;
- e) applying a noise reduction filter the box blur image;
- f) normalizing the noise reduced image;
- g) increasing the range between white (conjunctiva) and black (blood vessel) pixels on the normalized image;
- h) applying a Gaussian Matched filter to the range increased image;
- i) scoring each pixel of the Gaussian Matched image on a likelihood of being in a blood vessel;
- j) calculating, for each of the scored pixels, the best orthogonal angle;
- k) rank segmenting the results;
- i) identifying the best identified blood vessel candidates from the segmented rankings using a threshold;
- j) calculating a midpoint for each segment;
- k) calculating midpoints for the rank segmented pixels;
- m) calculating blood vessel diameters associated with each midpoint;
- n) chaining each identified midpoint to the other identified midpoints;
- o) calculating a line that connects and traverses the blood vessel through the chained midpoints creating a line;
- and
- p) performing feature analysis on the blood vessel using statistics and the blood vessel chains to identify and measure features in the micro circulation to identified diabetic conditions.
Dependencies are inferred from the claim text.